Abstract
This paper proposes a Markov random field(MRF) model-based method for unsupervised segmentation of images consisting of multiple textures. To model a textured image, a hierarchical MRF is used with two layers, the first layer representing an unobservable region image and the second layer representing multiple textures which cover each region. This method uses the Expectation and Maximization(EM) method for model parameter estimation, where in order to overcome the well-noticed computational problem in the expectation step, the Baum function is decomposed using the mean field approximation. Given provisionally estimated parameters at each iteration in the EM method, a provisional segmentation is carried out using the local a posteriori probability of each pixel's region label, which is also derived by mean-field-based decomposition of a posteriori probability of the whole region image.
